The present invention relates generally to computing systems, and relates more particularly to performance and systems management of computing systems. Specifically, the invention is a method and apparatus for online determination of sample intervals for optimization and control operations in a dynamic, on-demand computing environment.
FIG. 1 is a block diagram illustrating a typical data processing system 10. The data processing system 10 comprises a database server 100 which serves one or more database clients 150. The database server 100 includes a plurality of memory pools 121-125 that is adapted to cache data in a plurality of storage media 111-119. Database agents 101-109 access copies of storage media data through the memory pools 121 to 125 in order to serve the clients 150.
Central to the performance of the data processing system 10 is the management of the memory pools 121-125. Increasing the size of a memory pool 121-125 can dramatically reduce response time for accessing storage media data, since there is a higher probability that a copy of the data is cached in memory. This reduction in response time, measured in terms of saved response time per unit memory increase, is referred to as the “response time benefit” (or “benefit”).
A benefit reporter and a memory tuner operate to optimize the benefit derived from the system 10. At regularly scheduled intervals (referred to as “sample intervals”), the benefit reporter 130 collects measured output data (e.g., data indicative of system performance metrics) and transmits the data to the memory tuner 140, which is adapted to adjust memory pool allocations, based on analysis of the measured output data, with the intent of reducing overall response time for data access.
Due to the stochastic and dynamic nature of computing systems, the size of these sample intervals can be critical. For example, too small a sample interval may yield an insufficient collection of samples, and significant measurement noise may be generated during optimization, resulting in controller-introduced oscillation. On the other hand, too large a sample interval may reduce the optimization responsiveness as measured by time-response characteristics, such as system settling time. Effective online optimization therefore requires a substantially precise sample interval in order to provide fast response without introducing unwanted oscillation. A drawback of conventional systems for determining sample intervals, such as the benefit reporter and memory tuner system discussed above, is that the determinations tend to be based on static workloads. However, in a dynamic, on-demand environment, the workload characteristics and system configurations change drastically with time, and statically derived intervals may therefore yield less than optimal results.
Thus, there is a need in the art for a method and apparatus for online sample interval determination.